veloxx 0.4.0

Veloxx: High-performance, lightweight Rust library for in-memory data processing and analytics. Features DataFrames, Series, advanced I/O (CSV, JSON, Parquet), machine learning (linear regression, K-means, logistic regression), time-series analysis, data visualization, parallel processing, and multi-platform bindings (Python, WebAssembly). Designed for minimal dependencies, optimal memory usage, and blazing speed - ideal for data science, analytics, and performance-critical applications.
Documentation 
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# Usage

This package implements a GPU accelerated WebGL backend for TensorFlow.js.

## Importing the backend

Note: this backend is included by default in `@tensorflow/tfjs`.

### Via NPM

```js
// Import @tensorflow/tfjs-core
import * as tf from '@tensorflow/tfjs-core';
// Adds the WebGL backend to the global backend registry.
import '@tensorflow/tfjs-backend-webgl';
```

### Via a script tag

```html
<!-- Import @tensorflow/tfjs-core -->
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-core"></script>

<!-- Adds the WebGL backend to the global backend registry -->
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-webgl"></script>
```

You can also get ES2017 code using the following links

```html
<!-- Import @tensorflow/tfjs-core -->
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-core@2.0.0-rc.4/dist/tf-core.es2017.js"></script>

<!-- Adds the WebGL backend to the global backend registry -->
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-webgl@2.0.0-rc.4/dist/tf-backend-webgl.es2017.js"></script>
```